Abstract
Cell-based fluorescence imaging assays are heterogeneous requiring collection of a large number of images for detailed quantitative analysis. Complexities arise as a result of variation in spatial nonuniformity, shape, overlapping compartments, and scale. A new technique and methodology has been developed and tested for delineating subcellular morphology and partitioning overlapping compartments at multiple scales. This system is packaged as an integrated software platform for quantifying images that are obtained through fluorescence microscopy. Proposed methods are model-based, leveraging geometric shape properties of subcellular compartments and corresponding protein localization. From the morphological perspective, convexity constraint is imposed to delineate, partition, and group nuclear compartments. From the protein localization perspective, radial symmetry is imposed to localize punctate protein events at sub-micron resolution. The technique has been tested against 196 images that were generated to study centrosome abnormalities. Computed representations are evaluated against the ground truth annotation for comparative analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Malladi, R., Sethian, J.: A unified approach to noise removal, image enhancement, and shape recovery. IEEE Transactions on Image Processing 5(11), 1554–1568 (1995)
Medioni, G., Lee, M.S., Tang, C.K.: A Computational Framework for Segmentation and Grouping. Elsevier, Amsterdam (2000)
Murphy, R.: Automated interpretation of subcellular locatoin patterns. In: IEEE Int. Symp. on Biomedical Imaging, April 2004, pp. 53–56 (2004)
Ortiz De Solorzano, C., et al.: Segmentation of nuclei and cells using membrane protein. Journal of Microscopy 201, 404–415 (2001)
Parvin, B., Yang, Q., Fontenay, G., Barcellos-Hoff, M.: Biosig: An imaging bioinformatics system for phenotypic analysis. IEEE Transactions on Systems, Man and Cybernetics 33(B5), 814–824 (2003)
Reisfeld, D., Wolfson, H., Yeshurun, Y.: Context-free attentional operators: The generalized symmetry transform. IJCV 14(2), 119–130 (1995)
Reisfeld, D., Yeshurun, Y.: Preprocessing of face images: Detection of features and pose normalization. CVIU 71(3), 413–430 (1998)
Salisbury, J.L.: The contribution of epigenetic changes to abnormal centrosomes and genomic instability in breast cancer. Journal of Mammary Gland Biology and Neoplasia 6(2), 203–212 (2001)
Salisbury, J.L., D’Assoro, A.B., Lingle, W.L.: Centrosome amplification and the origin of chromosomal instability in breast cancer. Journal of Mammary Gland Biology and Neoplasia 9(3), 275–283 (2004)
Sela, G., Levine, M.D.: Real-time attention for robotic vision. Real-Time Imaging 3(3), 173–194 (1997)
Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. PAMI 13(6), 583–598 (1991)
Yang, Q., Parvin, B.: Harmonic cut and regularized centroid transform for localization of subceullar structures. IEEE Transactions on Biomedical Engineeing 50(4), 469–475 (2003)
Yang, Q., Parvin, B.: Perceptual organization of radial symmetries. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 320–325 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raman, S., Parvin, B., Maxwell, C., Barcellos-Hoff, M.H. (2005). Geometric Approach to Segmentation and Protein Localization in Cell Cultured Assays. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_52
Download citation
DOI: https://doi.org/10.1007/11595755_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30750-1
Online ISBN: 978-3-540-32284-9
eBook Packages: Computer ScienceComputer Science (R0)